Development of an application for recognizing mathematical formulas using convolutional neural networks

In the ever-expanding landscape of digital content, the ability to recognize and understand mathematical formulas is crucial for various applications, including academic research, document processing, and educational technology. This project introduces a prototype system designed to recognize mathematical formulas using the YOLOv5 (You Only Look Once) object detection framework. YOLOv5’s real-time object detection capabilities make it a promising choice for identifying and extracting mathematical expressions seamlessly from digital documents. The proposed prototype system addresses a critical gap in the field of OCR by focusing on the specialized domain of mathematical notation recognition. The YOLOv5-based approach offers real-time processing capabilities, making it suitable for applications that require quick and accurate identification of mathematical content in various digital contexts.